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The smart grid is an upgraded concept of electricity network with tight coupling among information, control, and bi‐directional communication technologies. Along with the silent features of the on‐going smart grid, cyber–physical security appears to be a deep concern due to its significant dependence on sensing technologies, complex networks of computers, intelligence, and wide‐area communication infrastructures. Moreover, the smart grid is an extensive critical infrastructure and vulnerable to coordinated cyber–physical attacks. As a result, cyber–physical attacks cause significant threats to the confidentiality and integrity of the power data, including power outages, cascading failures, and unnecessary expenditure. In this study, security issues of smart grid, including cyber–physical interdependency, attack varieties, detection methods, requirements, standards, challenges, and future prospects, are taken into consideration for both cyber and physical systems, aiming to provide an extensive understanding of vulnerabilities and solutions for the smart power grid. By revealing the inherent features of cyber–physical security in the smart grid, this survey study is addressed to facilitate future research in these two areas.

Autonomous and intelligent system show a remarkable step in urban traffic management. Autonomous Intersection Management (AIM) is an outstanding example of using an autonomous vehicle and wireless communication technology. The traffic performance of a single AIM system has been proved in many works however, traffic in the network of multiple AIMs is waiting for an implementation. Coordination of traffic between intersections in the network is an important step of managing the overall networked traffic throughput. The authors modeled the traffic network with the multi-agents concept and used the discrete consensus algorithm to coordinate between autonomous agents and implemented the rerouting algorithm in order to distribute the excessive traffics to neighbored intersections with the optimal condition. Our target is to have a balance traffic in each intersection and reaches the equilibrium where the stability has been not compromised. The results show that reaching consensus condition will bring the networked traffic to an equilibrium state where a peak traffic will not be happened. In addition, this method shows that when traffic in a network reached consensus, it will also converge to the Nash equilibrium in the finite time.

The Smart Grid aims to enhance the electric grid’s reliability, safety, and efficiency by utilizing digital information and control technologies. Real-time analysis and state estimation methods are crucial for ensuring proper control implementation. However, the reliance of Smart Grid systems on communication networks makes them vulnerable to cyberattacks, posing a significant risk to grid reliability. To mitigate such threats, efficient intrusion detection and prevention systems are essential. This paper proposes a hybrid deep-learning approach to detect distributed denial-of-service attacks on the Smart Grid’s communication infrastructure. Our method combines the convolutional neural network and recurrent gated unit algorithms. Two datasets were employed: The Intrusion Detection System dataset from the Canadian Institute for Cybersecurity and a custom dataset generated using the Omnet++ simulator. We also developed a real-time monitoring Kafka-based dashboard to facilitate attack surveillance and resilience. Experimental and simulation results demonstrate that our proposed approach achieves a high accuracy rate of 99.86%.

By some counts, up to 98% of environmental news stories are negative in nature. Implicit in this number is the conventional wisdom among many communicators that increasing people's understanding, awareness, concern or even fear of climate change are necessary precursors for action and behavior change. In this article we review scientific theories of mind and brain that explain why this conventional view is flawed. In real life, the relationship between beliefs and behavior often goes in the opposite direction: our actions change our beliefs, awareness and concerns through a process of self-justification and self-persuasion. As one action leads to another, this process of self-persuasion can go hand in hand with a deepening engagement and the development of agency-knowing how to act. One important source of agency is learning from the actions of others. We therefore propose an approach to climate communication and storytelling that builds people's agency for climate action by providing a wide variety of stories of people taking positive action on climate change. Applied at scale, this will shift the conceptualization of climate change from 'issue-based' to 'action-based'. It will also expand the current dominant meanings of 'climate action' (i.e. 'consumer action' and 'activism') to incorporate all relevant practices people engage in as members of a community, as professionals and as citizens. We close by proposing a systematic approach to get more reference material for action-based stories from science, technology and society to the communities of storytellers-learning from health communication and technologies developed for COVID-19.

For more than a decade, communication systems based on the IEEE 802.11p technology—often referred to as Dedicated Short-Range Communications (DSRC)—have been considered a de facto industry standard for Vehicle-to-Infrastructure (V2I) communication. The technology, however, is often criticized for its poor scalability, its suboptimal channel access method, and the need to install additional roadside infrastructure. In 3GPP Release 14, the functionality of existing cellular networks has been extended to support V2X use cases in an attempt to address the well-known drawbacks of the DSRC. In this paper, we present a complex simulation study in order to benchmark both technologies in a V2I communication context and an urban scenario. In particular, we compare the DSRC, LTE in the infrastructural mode (LTE-I), and LTE Device-to-Device (LTE-D2D) mode 3 in terms of the average end-to-end delay and Packet Delivery Ratio (PDR) under varying communication conditions achieved through the variation of the communication perimeter, message generation frequency, and road traffic intensity. The obtained results are put into the context of the networking and connectivity requirements of the most popular V2I C-ITS services. The simulation results indicate that only the DSRC technology is able to support the investigated V2I communication scenarios without any major limitations, achieving an average end-to-end delay of less than 100 milliseconds and a PDR above 96% in all of the investigated simulation scenarios. The LTE-I is applicable for the most of the low-frequency V2I services in a limited communication perimeter (<600 m) and for lower traffic intensities (<1000 vehicles per hour), achieving a delay pf less than 500 milliseconds and a PDR of up to 92%. The LTE-D2D in mode 3 achieves too great of an end-to-end delay (above 1000 milliseconds) and a PDR below 72%; thus, it is not suitable for the V2I services under consideration in a perimeter larger than 200 m. Moreover, the LTE-D2D mode 3 is very sensitive to the distance between the transmitter and its serving eNodeB, which heavily impacts the PDR achieved.

<p><b>A PRACTICAL GUIDE TO MICROGRID SYSTEMS ARCHITECTURE, DESIGN TOPOLOGIES, CONTROL STRATEGIES AND INTEGRATION APPROACHES</b> <p><i>Microgrid Planning and Design</i> offers a detailed and authoritative guide to microgrid systems. The editors &#150; noted experts on the topic &#150; explore what is involved in the design of a microgrid, examine the process of mapping designs to accommodate available technologies and reveal how to determine the efficacy of the final outcome. This practical book is a compilation of collaborative research results drawn from a community of experts in 8 different universities over a 6-year period. <p><i>Microgrid Planning and Design</i> contains a review of microgrid benchmarks for the electric power system and covers the mathematical modeling that can be used during the microgrid design processes. The authors include real-world case studies, validated benchmark systems and the components needed to plan and design an effective microgrid system. This important guide: <ul> <li>Offers a practical and up-to-date book that examines leading edge technologies related to the smart grid</li> <li>Covers in detail all aspects of a microgrid from conception to completion</li> <li>Explores a modeling approach that combines power and communication systems</li> <li>Recommends modeling details that are appropriate for the type of study to be performed</li> <li>Defines typical system studies and requirements associated with the operation of the microgrid</li> </ul> <p>Written for graduate students and professionals in the electrical engineering industry, <i>Microgrid Planning and Design</i> is a guide to smart microgrids that can help with their strategic energy objectives such as increasing reliability, efficiency, autonomy and reducing greenhouse gases.

With the rapidly growing energy consumption and the rising number of prosumers, next-generation energy management systems are facing significant impacts by peer-to-peer (P2P) energy trading, which will enable prosumers to sell and purchase energy locally. Until now, the large-scale deployment of P2P energy trading has still posed many technical challenges for both physical and virtual layers. Although the communication infrastructure represents the cornerstone to enabling real-time monitoring and control, less attention has been given to the performance of different communication technologies to support P2P implementations. This work investigates the scalability and performance of the communication infrastructure that supports P2P energy trading on a community microgrid. Five levels make up the developed P2P architecture: the power grid, communication network, cloud management, blockchain, and application. Based on the IEC 61850 standard, we developed a communication network model for a smart consumer that comprised renewable energy sources and energy storage devices. Two different scenarios were investigated: a home area network for a smart prosumer and a neighborhood area network for a community-based P2P architecture. Through simulations, the suggested network models were assessed for their channel bandwidth and end-to-end latency utilizing different communication technologies.

Transportation agencies continuously and consistently work to improve the processes and systems for mitigating the impacts of roadway incidents. Such efforts include utilizing emerging technologies to reduce the detection and response time to roadway incidents. Vehicle-to-infrastructure (V2I) communication is an emerging transportation technology that enables communication between a vehicle and the infrastructure. This paper proposes an algorithm that utilizes V2I probe data to automatically detect roadway incidents. A simulation testbed was developed for a segment of Interstate 64 in St. Louis, Missouri to evaluate the performance of the V2I-based automatic incident detection algorithm. The proposed algorithm was assessed during peak and off-peak periods with various incident durations, under several market penetration rates for V2I technology, and with different spatial resolutions for incident detection. The performance of the proposed algorithm was assessed on the basis of the detection rate, time to detect, detection accuracy, and false alarm rate. The performance measures obtained for the V2I-based automatic incident detection algorithm were compared with California #7 algorithm performance measures. The California #7 algorithm is a traditional automatic incident detection algorithm that utilizes traffic sensors data, such as inductive loop detectors, to identify roadway events. The California #7 algorithm was implemented in the Interstate 64 simulation testbed. The case study results indicated that the proposed V2I-based algorithm outperformed the California #7 algorithm. The detection rate for the proposed V2I-based incident detection algorithm was 100% in market penetrations of 50%, 80%, and 100%. However, the California #7 algorithm’s detection rate was 71%.

As transportation systems evolve, ensuring safe and efficient mobility in Intelligent Transportation Systems remains a priority. Work zones, in particular, pose significant safety challenges due to lane closures, which can lead to abrupt braking and sudden lane changes. Most previous research on Connected and Autonomous Vehicles (CAVs) assumes ideal communication conditions, overlooking the effects of message loss and network unreliability. This study presents a comprehensive smart work zone (SWZ) framework that enhances lane-change safety by the integration of both Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. Sensor-equipped SWZ barrels and Roadside Units (RSUs) collect and transmit real-time hazard alerts to approaching CAVs, ensuring coverage of critical roadway segments. In this study, a co-simulation framework combining VEINS, OMNeT++, and SUMO is implemented to assess lane-change safety and communication performance under realistic network conditions. Findings indicate that higher Market Penetration Rates (MPRs) of CAVs can lead to improved lane-change safety, with time-to-collision (TTC) values shifting toward safer time ranges. While lower transmission thresholds allow more frequent communication, they contribute to earlier network congestion, whereas higher thresholds maintain efficiency despite increased packet loss at high MPRs. These insights highlight the importance of incorporating realistic communication models when evaluating traffic safety in connected vehicle environments.

Optimizing the deployment of roadside units (RSUs) holds great potential for enhancing the delay performance of vehicular ad hoc networks. However, there has been limited focus on devising RSU deployment strategies tailored specifically for highway intersections. In this study, we introduce a novel probabilistic model to characterize events occurring around highway intersections. By leveraging this model, we analytically determine the expected event reporting delays for both highway segments and intersections. Subsequently, we propose an RSU deployment scheme specifically designed for highway intersections, aimed at minimizing the expected event reporting delay. To implement this scheme, we introduce an innovative algorithm named cooperative walking. Through illustrative examples, we demonstrate that our proposed RSU deployment strategy for highway intersections outperforms the commonly employed uniform RSU deployment scheme and the previously proposed balloon method in terms of delay performance.